A New Approach to Word Sense Disambiguation in MT System

Author

Zheng, Zhang ; Shu, Zhu

Author_Institution

Sch. of Foreign Languages, Beijing Technol. & Bus. Univ., Beijing, China

Volume

7

fYear

2009

fDate

March 31 2009-April 2 2009

Firstpage

407

Lastpage

411

Abstract

The word sense disambiguation (WSD) is a tough issue in natural language processing. This paper introduces and contrasts the main approaches of WSD prevailing in the world, and analyzes their advantages and disadvantages briefly. Then the author focuses on the vector space model (VSM), and furthermore, puts forward a new method that uses the approach of multi-level sentence similarity (MLSS) computation in the VSM. The new method improves the accuracy of VSM method and overcomes the "bag of words" problem in VSM.

Keywords

language translation; natural language processing; vectors; machine translation; multilevel sentence similarity computation; natural language processing; vector space model; word sense disambiguation; Computer science; Dictionaries; Educational institutions; Explosions; Large-scale systems; Learning systems; Natural language processing; Space technology; Supervised learning; Unsupervised learning; machine translation (MT); sentence similarity; vector space model (VSM); word sense disambiguation (WSD);

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Science and Information Engineering, 2009 WRI World Congress on

Conference_Location

Los Angeles, CA

Print_ISBN

978-0-7695-3507-4

Type

conf

DOI

10.1109/CSIE.2009.1105

Filename

5170352